Spectroscopic imaging combines digital imaging and molecular spectroscopy techniques including Raman scattering, fluorescence, photoluminescence, ultraviolet, visible and infrared absorption spectroscopies. When such techniques are applied to the chemical analysis of materials, spectroscopic imaging is commonly referred to as chemical imaging or molecular imaging. Instruments for performing spectroscopic, e.g. chemical, imaging typically comprise an illumination source, an image gathering optic, a focal plane array imaging detector and an image spectrometer.
Generally, the sample size determines the choice of image gathering optic. For example, a microscope is typically employed to analyze sub-micron to millimeter spatial dimension samples. In the case of larger objects, in the range of millimeter to meter dimensions, macro-lens optics are appropriate. For samples located within relatively inaccessible environments, flexible fiberscope or rigid borescopes may be employed. Further, for very large scale objects, such as planetary objects, telescopes are appropriate image gathering optics.
For detection of images formed by the various optical systems, two-dimensional, imaging focal plane array (“FPA”) detectors are typically employed. The choice of FPA detectors is governed by the spectroscopic technique employed to characterize the sample of interest. For example, silicon (Si) charge-coupled device (“CCD”) detectors or CMOS detectors are typically employed with visible wavelength fluorescence and Raman spectroscopic imaging systems, while indium gallium arsenide (“InGaAs”) FPA detectors are typically employed with near-infrared spectroscopic imaging systems.
Spectroscopic imaging of a sample is commonly implemented by one of two methods. First, point-source illumination may be used on a sample to measure the spectrum at each point of the illuminated area. Second, spectra can be collected over the entire area encompassing a sample simultaneously using an electronically tunable optical imaging filter such as an acousto-optic tunable filter (“AOTF”), a multi-conjugate tunable filter (“MCF”), or a liquid crystal tunable filter (“LCTF”). Here, the organic material in such optical filters is actively aligned by applied voltages to produce the desired bandpass and transmission function. The spectrum obtained for each pixel of an image forms a complex data set referred to as a hyperspectral image. Hyperspectral images may contain the intensity values at numerous wavelengths or the wavelength dependence of each pixel element in the image. Multivariate routines, such as chemometric techniques, may be used to convert spectra to classifications.
Spectroscopic devices operate over a range of wavelengths dependent on the operation ranges of the detectors or tunable filters employed. The devices may operate and provide analysis in the Ultraviolet (UV), visible (VIS), near infrared (NIR), short-wave infrared (SWIR), midwave infrared (MWIR), and/or long wave infrared (LWIR) wavelength ranges, including some overlapping ranges. These ranges correspond to wavelengths of approximately 180-380 nm (UV), 380-700 nm (VIS), 700-2500 nm (NIR), 850-1800 nm (SWIR), 650-1100 nm (MWIR), 400-1100 nm (VIS-NIR) and 1200-2450 nm (LWIR).
A LCTF employs birefringent retarders to distribute the light energy of an input light signal over a range of polarization states. The polarization state of light emerging at the output of the LCTF is caused to vary as a function of wavelength due to differential retardation of the orthogonal components of light, contributed to by the birefringent retarders. The LCTF discriminates for wavelength-specific polarization using a polarizing filter at the output. The polarizing filter passes the light components through the output that are rotationally aligned to the polarizing filter. The LCTF is tuned by adjusting the birefringence of the retarders so that a specific discrimination wavelength that is aligned to the output polarizing filter emerges in a plane polarized state. Other wavelengths that emerge in other polarization states and/or alignments are attenuated.
A highly discriminating spectral filter is possible using a sequence of several birefringent retarders. The thicknesses, birefringences, and relative rotation angles of the retarders are chosen to correspond to the discrimination wavelength. More specifically, the input light signal to the filter becomes separated into orthogonal vector components, parallel to the respective ordinary and extraordinary axes of each birefringent retarder when encountered along the light transmission path through the filter. These separated vector components are differentially retarded due to the birefringence. Such differential retardation also amounts to a change in the polarization state. For a plane polarized component at the input to the filter, having a specific rotational alignment at the input to the filter and at specific discrimination wavelengths, the light components that have been divided and subdivided all emerge from the filter in the same polarization state and alignment, namely plane polarized and in alignment with the selection polarizer, i.e., the polarizing filter, at the output.
A filter as described is sometimes termed an interference filter due to the components being divided and subdivided from the input and interfering positively at the output selection polarizer are the components that are passed through the filter. Such filters are sometimes described with respect to a rotational twist in the plane polarization alignment of the discriminated component between the input and the selection polarizer at the output.
There are several known configurations of spectral filters comprising birefringent retarders and such filters include, for example, Lyot, Solc, and Evans types. These filters can be constructed with fixed (non-tunable) birefringent crystals as retarders. A filter with retarders that are tuned in unison will permit adjustment of the bandpass wavelength. Tunable retarders can comprise liquid crystals or composite retarder elements, each comprising a fixed crystal and an optically aligned liquid crystal.
The birefringences and rotation angles of the retarders are coordinated such that each retarder contributes part of the necessary change in polarization state to alter the polarization state of the passband wavelength from an input reference angle to an output reference angle. The input reference angle may be, for example, 45° to the ordinary and extraordinary axes of a first retarder in the filter. The output reference angle is the rotational alignment of the polarizing filter, i.e., “selection polarizer.”
A spectral filter may have a comb-shaped transmission characteristic. Increasing or decreasing the birefringence while tuning to select the discrimination wavelength (or passband), stretches or compresses the comb shape of the transmission characteristic along the wavelength coordinate axis.
If the input light is randomly polarized, the portion that is spectrally filtered is limited to the vector components of the input wavelengths that are parallel to one of the two orthogonal polarization components that are present. Only light at the specific wavelength, and at a given reference polarization alignment at the input, can emerge with a polarization angle aligned to the rotational alignment of the selection polarizer at the output. The light energy that is orthogonal to the reference alignment at the input, including light at the passband wavelength, is substantially blocked.
Currently, tunable optical filter technology is limited to single bandpass, low throughput operation and passes only one of two orthogonal components of input light. The transmission ratio in the passband is at a maximum for incident light at the input to the LCTF that is aligned to a reference angle of the LCTF. Transmission is at a minimum for incident light energy at the input that is orthogonal to that reference angle. If the input light in the passband is randomly polarized, the best possible transmission ratio in the passband is fifty percent. In addition, multiple discrete bandpass measurements are required for tissue type discrimination. The need for multiple measurements translates directly into overall measurement time.
Multivariate Optical Computing is an approach which utilizes a compressive sensing device, e.g. an optical computer, to analyze spectroscopic data as it is collected. Other approaches utilize hard coated optical computing filters such as Multivariate Optical Elements (“MOEs”). MOEs are application-specific optical thin film filters that are used in transmission and reflectance modes. The radiometric response of a MOE-based instrument is proportional to the intended tissue type in an associated matrix. While compressive sensing holds potential for decreasing measurement time, the use of MOEs has limitations. For example, MOEs are fixed and lack flexibility for adapting to different tissue types.
Cancer is an enormous global health burden, accounting for one in every eight deaths worldwide. A critical problem in cancer management is the local recurrence of disease, which is often a result of incomplete excision of the tumor. Currently, tumor margins must be identified through histological evaluation of an affected tissue biopsy post-surgery. As such, approximately one in four patients who undergo tumor resection surgery will require a follow-up operation in order to fully excise the malignant tissue. Recent efforts aimed towards significantly reducing the frequency of local recurrence have employed diffuse reflectance, radiofrequency spectroscopy, and targeted fluorescence imaging. However, there remains an urgent need to develop a highly specific and sensitive tool to detect features in biological tissues, including intraoperative real-time tumor margin detection methods that will reduce the risk of cancer recurrence and the need for subsequent operations.
Current techniques for gross anatomic pathology require inspection by a pathologist and are therefore inherently subjective. There exists a need for a system and method that would enable objective analysis of organ samples and other biological tissues. It would also be advantageous if such a system and method were designed as an intra-operative tool, providing both molecular and spatial information. There exists a need for an adaptable filter that can be used to detect a wide variety of tissue types while reducing overall measurement time. It would be advantageous if the filter could be incorporated into a system for biomedical applications such as intraoperative applications.